[show abstract][hide abstract] ABSTRACT: Terrestrial gross primary production (GPP) is an important parameter to explore and quantify carbon fixation by plant ecosystems at various scales. Remote sensing (RS) offers a unique possibility to investigate GPP in a spatially explicit fashion; however, budgeting of terrestrial carbon cycles based on this approach still remains uncertain. To improve calculations, spatio-temporal variability of GPP must be investigated in more detail on local and regional scales. The overarching goal of this study is to enhance our knowledge on how environmentally induced changes of photosynthetic light-use efficiency (LUE) are linked with optical RS parameters. Diurnal courses of sun-induced fluorescence yield (FSyield) and the photochemical reflectance index of corn were derived from high-resolution spectrometric measurements and their potential as proxies for LUE was investigated. GPP was modeled using Monteith's LUE-concept and optical-based GPP and LUE values were compared with synoptically acquired eddy covaria
Global Change Biology 10/2013; 16(1):171-186. · 6.91 Impact Factor
[show abstract][hide abstract] ABSTRACT: The main objective of FLEX is the measurement of vegetation chlorophyll fluorescence (Fs) from space and the exploitation of this signal to better understand the carbon cycle. FLuORescence Imaging Spectrometer (FLORIS) is the main instrument of the FLEX mission concept. ESA's Earth Science Advisory Committee recommended the investigation of the FLEX concept as an in-orbit demonstrator to be flown as a tandem mission with Sentinel-3 (S-3). S-3 is amongst others equipped with the Ocean Land Colour Instrument (OLCI). When flown in tandem these instruments are expected to provide an accurate characterization of key atmospheric and surface parameters to facilitate Fs retrieval for FLORIS. In this work the performance of FLORIS and S3-OLCI sensors and their synergy was evaluated on their capability of retrieving relevant biophysical parameters using simulated top-of-atmosphere radiance data (LTOA). For both sensors, LTOA data were simulated across a wide range of vegetation, atmospheric and geometry parameters by coupling leaf, canopy and atmospheric radiative transfer models. The pursued analysis was to train for each retrievable parameter (here: Chl, LAI, soil type and Ftotal) a regression model using the simulated datasets and then evaluate its performance. Two regression types were chosen, a conventional linear regressor and a more advanced nonlinear regressor, and two types of training/validation strategies were followed: a local strategy (at least 2 parameters fixed) and a generic strategy (uniform random subset of the complete dataset). The simulation study led to the following conclusions: 1) FLORIS is well equipped for accurate retrieval of biophysical parameters; 2) however, advanced nonlinear regressors may be needed to achieve robust results, and 3) the large number of bands can lead to redundancy in the nonlinear regressors which can be overcomed by band optimization strategies. Finally, 4) it was demonstrated that a synergy- of both FLORIS and S3-OLCI datasets leads to improved biophysical parameter retrieval.
[show abstract][hide abstract] ABSTRACT: Radiative transfer (RT) modeling plays a key role for earth observation
(EO) because it is needed to design EO instruments and to develop and
test inversion algorithms. The inversion of a RT model is considered as
a successful approach for the retrieval of biophysical parameters
because of being physically-based and generally applicable. However, to
the broader community this approach is considered as laborious because
of its many processing steps and expert knowledge is required to realize
precise model parameterization. We have recently developed a radiative
transfer toolbox ARTMO (Automated Radiative Transfer Models Operator)
with the purpose of providing in a graphical user interface (GUI)
essential models and tools required for terrestrial EO applications such
as model inversion. In short, the toolbox allows the user: i) to choose
between various plant leaf and canopy RT models (e.g. models from the
PROSPECT and SAIL family, FLIGHT), ii) to choose between spectral band
settings of various air- and space-borne sensors or defining own sensor
settings, iii) to simulate a massive amount of spectra based on a look
up table (LUT) approach and storing it in a relational database, iv) to
plot spectra of multiple models and compare them with measured spectra,
and finally, v) to run model inversion against optical imagery given
several cost options and accuracy estimates. In this work ARTMO was used
to tackle some well-known problems related to model inversion. According
to Hadamard conditions, mathematical models of physical phenomena are
mathematically invertible if the solution of the inverse problem to be
solved exists, is unique and depends continuously on data. This
assumption is not always met because of the large number of unknowns and
different strategies have been proposed to overcome this problem.
Several of these strategies have been implemented in ARTMO and were here
analyzed to optimize the inversion performance. Data came from the
SPARC-2003 dataset, which was acquired on the agricultural test site
Barrax, Spain. LUTs were created using the 4SAIL and FLIGHT models and
were inverted against CHRIS data in order to retrieve maps of
chlorophyll content (chl) and leaf area index (LAI). The following
inversion steps have been optimized: 1. Cost function. The performances
of about 50 different cost functions (i.e. minimum distance functions)
were compared. Remarkably, in none of the studied cases the widely used
root mean square error (RMSE) led to most accurate results. Depending on
the retrieved parameter, more successful functions were: 'Sharma and
Mittal', 'Shannońs entropy', 'Hellinger distance',
'Pearsońs chi-square'. 2. Gaussian noise. Earth observation data
typically encompass a certain degree of noise due to errors related to
radiometric and geometric processing. In all cases, adding 5% Gaussian
noise to the simulated spectra led to more accurate retrievals as
compared to without noise. 3. Average of multiple best solutions.
Because multiple parameter combinations may lead to the same spectra, a
way to overcome this problem is not searching for the top best match but
for a percentage of best matches. Optimized retrievals were encountered
when including an average of 7% (Chl) to 10% (LAI) top best matches.
4. Integration of estimates. The option is provided to integrate
estimates of biochemical contents at the canopy level (e.g., total
chlorophyll: Chl × LAI, or water: Cw × LAI), which can lead
to increased robustness and accuracy. 5. Class-based inversion. This
option is probably ARTMÓs most powerful feature as it allows
model parameterization depending on the imagés land cover classes
(e.g. different soil or vegetation types). Class-based inversion can
lead to considerably improved accuracies compared to one generic class.
Results suggest that 4SAIL and FLIGHT performed alike for Chl but not
for LAI. While both models rely on the leaf model PROSPECT for Chl
retrieval, their different nature (e.g. numerical vs. ray tracing) may
cause that retrieval of structural parameters such as LAI differ.
Finally, it should be noted that the whole analysis can be intuitively
performed by the toolbox. ARTMO is freely available to the EO community
for further development. Expressions of interest are welcome and should
be directed to the corresponding author.
[show abstract][hide abstract] ABSTRACT: Air pollutant concentrations in cities can be very high due to the heavy traffic load. Also, these concentrations vary on a small scale due to differences in traffic density, street layout and the surrounding land uses, leading to local hot-spots for air pollution. These different growing environments can result in a different stress outcome for city trees, which can be measured at different scales. Leaf radiative transfer characteristics such as spectral reflectance and sun-induced chlorophyll fluorescence (F) give information about the physiological status of the leaf and can be measured both at the leaf and canopy scales. The goal of the BIOHYPE project is to develop a passive biomonitoring methodology from leaf to canopy scale based on fluorescence and reflectance parameters as indicators for leaf physiological stress. Field and flight campaigns were set in Valencia during the summer of 2011. Four tree species with different leaf characteristics were selected at 10 locations in the city with different traffic densities. Fluorescence emission was measured with an ASD FieldSpec spectroradiometer in combination with the Fluowat leafclip, a portable device to measure leaf reflectance, transmittance and fluorescence emission under natural conditions. Airborne images were acquired using a CASI-1500 VNIR hyperspectral imager in tandem with an AHS system for SWIR-TIR. Besides fluorescence, the following parameters have been measured and analyzed at leaf level: optical properties, chlorophyll content (Chl), water content and magnetic properties of deposited pollution dust. In this work, relationships of fluorescence with location (i.e. traffic density), pollution and chlorophyll content have been explored. At leaf level, first results suggest that the up- and down-ward total F yields are related to location for two of the four species, while the fluorescence peaks and their ratios showed a larger influence of location. The ratio F687/F741 for both down- and upward fluorescence showed the highest dependency to location. The relation between fluorescence peaks and Chl was subsequently investigated. It was found that the magnitude of the ratio F687/F741 is affected by the chlorophyll content of the species. Plane tree (Platanus hispanica) had a generally lower Chl compared to the other species and therefore showed a much larger response in F687/F741. Also, the downward total fluorescence yield for this species is closer to the upward fluorescence yield compared to the other species with more Chl. At the canopy scale, similar measurements were carried out at three different levels (lower, middle and upper canopy) to evaluate the variation in fluorescence emission within a single tree. This will enable us to upscale the results to the CASI imagery. Maps of Chl are already generated. In the near future it is foreseen to be able to retrieve the fluorescence-related products (e.g. F687/F741) over the city of Valencia and interpret these results to plant stress maps.
[show abstract][hide abstract] ABSTRACT: Earth observation satellites currently provide a large volume of images at different scales. Most of these satellites provide global coverage with a revisit time that usually depends on the instrument characteristics and performance. Typically, medium-spatial-resolution instruments provide better spectral and temporal resolutions than mapping-oriented high-spatial-resolution multispectral sensors. However, in order to monitor a given area of interest, users demand images with the best resolution available, which cannot be reached using a single sensor. In this context, image fusion may be effective to merge information from different data sources. In this letter, an image fusion approach based on multiresolution and multisource spatial unmixing is used to obtain a composite image with the spectral and temporal characteristics of medium-spatial-resolution instrument along with the spatial resolution of high-spatial-resolution image. A time series of Landsat/TM and ENVISAT/MERIS Full Resolution images acquired in the 2004 European Space Agency (ESA) Spectra Barrax Campaign illustrates the method's capabilities. The qualitative and quantitative assessments of the product images are given. The proposed methodology is general enough to be applied to similar sensors, such as the multispectral instruments which will fly on board the ESA GMES Sentinel-2 and Sentinel-3 upcoming satellite series.
[show abstract][hide abstract] ABSTRACT: Nowadays, the increasing quantity of applications using images from Earth Observation satellites makes demanding better spatial, spectral and temporal resolutions. Nevertheless, due to the technical constraint of a trade off between spatial and spectral resolutions, and between spatial resolution and coverage, high spatial resolution is related with low spectral and temporal resolutions and vice versa. Data fusion methods are a good solution to combine information from multiple sensors in order to obtain image products with better characteristics. In this paper, we propose an image fusion approach based on a multi-resolution and multi-source unmixing. The proposed methodology yields a composite image with the spatial resolution of the higher resolution image (downscaling) while retaining the spectral and temporal characteristics of the medium spatial resolution image. The approach is tested in the specific cases of ENVISAT/MERIS and Landsat/TM instruments, but is general enough to be applied to other sensor combination.
Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International; 08/2010
[show abstract][hide abstract] ABSTRACT: The Fraunhofer Line Discriminator (FLD) principle has long been considered as the reference method to quantify solar-induced chlorophyll fluorescence (F) from passive remote sensing measurements. Recently, alternative retrieval algorithms based on the spectral fitting of hyperspectral radiance observations, Spectral Fitting Methods (SFMs), have been proposed. The aim of this manuscript is to investigate the performance of such algorithms and to provide relevant information regarding their use. FLD and SFMs were used to estimate F starting from Top Of Canopy (TOC) fluxes at very high spectral resolution (0.12 nm) and sampling interval (0.1 nm), exploiting the O2-B (687.0 nm) and O2-A (760.6 nm) atmospheric oxygen absorption bands overlapping the fluorescence emissions at the red and far-red spectral window.Specific parameters affecting FLD and SFM performances are investigated and the accuracy of F estimation of the two methods is compared. The problem related to the lack of independent measurements of F at canopy level, which prevents the direct assessment of F estimation accuracy with actual measurements, is overcome in this study by using a modeled database of TOC reflectance spectra. In order to compute accuracy figures valid for operative applications the simulated spectra were perturbed by the addition of radiometric noise.An investigation was conducted to determine the best FLD channel configuration; it showed that violation of FLD assumptions results in a positive bias in F estimation at both oxygen absorption bands that cannot be avoided even at the high spectral resolution considered. SFMs were shown to be more accurate than FLD under any noise configuration considered.
Remote Sensing of Environment 03/2010; 114(2):363-374. · 5.10 Impact Factor
[show abstract][hide abstract] ABSTRACT: This paper presents a cloud screening algorithm based on ensemble methods that exploits the combined information from both MERIS and AATSR instruments on board ENVISAT in order to improve current cloud masking products for both sensors. The first step is to analyze the synergistic use of MERIS and AATSR images in order to extract some physically-based features increasing the separability of clouds and surface. Then, several artificial neural networks are trained using different sets of input features and different sets of training samples depending on acquisition and surface conditions. Finally, outputs of the trained neural networks are combined at the decision level to construct a more accurate and robust ensemble of classifiers. The proposed classifier is tested on more than 80 coregistered MERIS/AATSR images providing better classification accuracy than the official cloud flags and available operational cloud screening algorithms for MERIS and AATSR. Moreover, thanks to the synergy of both sensors, it correctly classifies critical cloud-screening problems such as snow and ice covers over land and sun-glint over ocean.
Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009; 08/2009
[show abstract][hide abstract] ABSTRACT: The AHS (Airborne Hyperspectral Scanner) instrument has 80 spectral bands covering the visible and near infrared (VNIR), short wave infrared (SWIR), mid infrared (MIR) and thermal infrared (TIR) spectral range. The instrument is operated by Instituto Nacional de ecnica Aerospacial (INTA), and it has been involved in several field campaigns 5 since 2004. This paper presents an overview of the work performed with the AHS thermal im-agery provided in the framework of the SPARC and SEN2FLEX campaigns, carried out respectively in 2004 and 2005 over an agricultural area in Spain. The data collected in both campaigns allowed for the first time the development and testing of algorithms 10 for land surface temperature and emissivity retrieval as well as the estimation of evap-otranspiration from AHS data. Errors were found to be around 1.5 K for land surface temperature and 1 mm/day for evapotranspiration.
[show abstract][hide abstract] ABSTRACT: The AHS (Airborne Hyperspectral Scanner) instrument has 80 spectral bands covering the visible and near infrared (VNIR), short wave infrared (SWIR), mid infrared (MIR) and thermal infrared (TIR) spectral range. The instrument is operated by Instituto Nacional de Técnica Aerospacial (INTA), and it has been involved in several field campaigns since 2004. This paper presents an overview of the work performed with the AHS thermal imagery provided in the framework of the SPARC and SEN2FLEX campaigns, carried out respectively in 2004 and 2005 over an agricultural area in Spain. The data collected in both campaigns allowed for the first time the development and testing of algorithms for land surface temperature and emissivity retrieval as well as the estimation of evapotranspiration from AHS data. Errors were found to be around 1.5 K for land surface temperature and 1 mm/day for evapotranspiration.
Hydrology and Earth System Sciences 01/2009; · 3.59 Impact Factor
[show abstract][hide abstract] ABSTRACT: Interest in remote sensing (RS) of solar-induced chlorophyll fluorescence (F) by terrestrial vegetation is motivated by the link of F to photosynthetic efficiency which could be exploited for large scale monitoring of plant status and functioning. Today, passive RS of F is feasible with different prototypes and commercial ground-based, airborne, and even spaceborne instruments under certain conditions. This interest is generating an increasing number of research projects linking F and RS, such as the development of new F remote retrieval techniques, the understanding of the link between the F signal and vegetation physiology and the feasibility of a satellite mission specifically designed for F monitoring. This paper reviews the main issues to be addressed for estimating F from RS observations. Scattered information about F estimation exists in the literature. Here, more than 40 scientific papers dealing with F estimation are reviewed and major differences are found in approaches, instruments and experimental setups. Different approaches are grouped into major categories according to RS data requirements (i.e. radiance or reflectance, multispectral or hyperspectral) and techniques used to extract F from the remote signal. Theoretical assumptions. advantages and drawbacks of each method are outlined and provide perspectives for future research. Finally, applications of the measured F signal at the three scales of observation (ground, aircraft and satellite) are presented and discussed to provide the state of the art in F estimation. (C) 2009 Elsevier Inc. All rights reserved.
REMOTE SENSING OF ENVIRONMENT. 01/2009; 113(10):2037-2051.
[show abstract][hide abstract] ABSTRACT: The CEFLES2 campaign during the Carbo Europe Regional Experiment Strategy was designed to provide simultaneous airborne measurements of solar induced fluorescence and CO2 fluxes. It was combined with extensive ground-based quantification of leaf- and canopy-level processes in support of ESA's Candidate Earth Explorer Mission of the "Fluorescence Explorer" (FLEX). The aim of this campaign was to test if fluorescence signal detected from an airborne platform can be used to improve estimates of plant mediated exchange on the mesoscale. Canopy fluorescence was quantified from four airborne platforms using a combination of novel sensors: (i) the prototype airborne sensor AirFLEX quantified fluorescence in the oxygen A and B bands, (ii) a hyperspectral spectrometer (ASD) measured reflectance along transects during 12 day courses, (iii) spatially high resolution georeferenced hyperspectral data cubes containing the whole optical spectrum and the thermal region were gathered with an AHS sensor, and (iv) the first employment of the high performance imaging spectrometer HYPER delivered spatially explicit and multi-temporal transects across the whole region. During three measurement periods in April, June and September 2007 structural, functional and radiometric characteristics of more than 20 different vegetation types in the Les Landes region, Southwest France, were extensively characterized on the ground. The campaign concept focussed especially on quantifying plant mediated exchange processes (photosynthetic electron transport, CO2 uptake, evapotranspiration) and fluorescence emission. The comparison between passive sun-induced fluorescence and active laser-induced fluorescence was performed on a corn canopy in the daily cycle and under desiccation stress. Both techniques show good agreement in detecting stress induced fluorescence change at the 760 nm band. On the large scale, airborne and ground-level measurements of fluorescence were compared on several vegetation types supporting the scaling of this novel remote sensing signal. The multi-scale design of the four airborne radiometric measurements along with extensive ground activities fosters a nested approach to quantify photosynthetic efficiency and gross primary productivity (GPP) from passive fluorescence.
[show abstract][hide abstract] ABSTRACT: This letter presents a modification to the established Fraunhofer line discrimination (FLD) method for improving the accuracy of the solar-induced chlorophyll fluorescence (ChF) retrieval over terrestrial vegetation. The FLD method relies on the decoupling of reflected and ChF emitted radiation by the evaluation of measurements inside and outside the absorption bands. The improved FLD method introduces two correction coefficients that relate the values of the fluorescence and the reflectance inside and outside the absorption band. The new method uses the full spectral information around the absorption band to derive these coefficients. A sensitivity analysis has been performed to evaluate the impact of the correction coefficients on the accuracy of the ChF estimation. The new formulation has been tested for the O<sub>2</sub> A-band on synthetic data obtaining lower errors in comparison to the standard FLD and has been successfully applied to real measurements at canopy level.
[show abstract][hide abstract] ABSTRACT: FLEX (FLuorescence EXperiment) is a candidate mission for the European Space Agency (ESA) Earth Explorer program. The main objective of the mission is the measurement the chlorophyll fluorescence signal emitted by vegetation at the red and far-red spectral regions (roughly 630-770 nm). The current FLEX mission design includes different instruments intended to provide the appropriate characterization of those atmospheric and surface parameters necessary for the retrieval and interpretation of the fluorescence signal. The complete processing chain for the derivation of fluorescence and reflectance products from the radiance data acquired by the different instruments included in the FLEX pay-load is described in this paper. Six processing modules have been implemented: cloud screening, aerosol optical thickness (AOT) retrieval, automatic spectral characterisation, columnar water vapor (CWV) retrieval, fluorescence retrieval and reflectance retrieval. The processing chain has been tested against a scene-based simulated data set which reproduces FLEX instruments and realistic atmospheric conditions.